Elastic's AI architecture guide for IT leaders to scale reliably
IT leaders waste 60% of AI projects without AI-ready data. Here's how to fix it.
Deep Dive
Elastic outlines four foundational AI architecture elements for reliable scaling, focusing on data preparation, context engineering, and governance with LLM observability built in from the start. Data quality is critical—Gartner predicts 60% of AI projects will be abandoned through 2026 without AI-ready data. Context engineering ensures models retrieve the right information, while embedded governance controls costs, security, and trust.
Key Points
- Gartner predicts 60% of AI projects will fail by 2026 without AI-ready data infrastructure
- Context engineering via RAG and vector databases cuts model costs by avoiding irrelevant data overload
- LLM observability and governance reduce operational drift and security risks in agentic AI systems
Why It Matters
IT leaders can avoid costly AI failures by building on solid data, context, and governance foundations—critical for scaling agentic AI reliably.